NDLI: Compact fuzzy rules induction and feature extraction using SVM with particle swarms for breast cancer treatments

Content Provider	IEEE Xplore Digital Library
Author	Shang-Ming Zhou John, R.I. Wang, X.-Y. Garibaldi, J.M. Ellis, I.O.
Copyright Year	2008
Description	Author affiliation: Centre for Comput. Intell., De Montfort Univ., Leicester (Shang-Ming Zhou; John, R.I.)
Abstract	Developing a treatment plan for breast cancer patient is a very complex process. In this paper, we propose a scheme of inducing fuzzy rules that characterise breast cancer treatment knowledge from data. These fuzzy rules can augment the human experts in the process of medical diagnosis to select optimal treatment for patients. The proposed machine learning scheme applies the particle swarm optimisation technique (PSO) to the construction of an optimal support vector machine (SVM) model for the sake of inducing accurate and parsimonious fuzzy rules and simultaneously reducing input space dimensions, in which a new fittness function that regularises the importance ranks of features with misclassification rate is suggested. The SVM-based fuzzy classifier evades the curse of dimensionality in high-dimensional breast cancer data space in the sense that the number of support vectors, which equals the number of induced fuzzy rules, is not related to the dimensionality. The experiments have shown that not only the classification performance achieved by the proposed fuzzy classifier outperforms the ones achieved by other methods in the literature, but also the input space dimension has been reduced greatly.
Starting Page	1469
Ending Page	1475
File Size	165504
Page Count	7
File Format	PDF
ISBN	9781424418220
DOI	10.1109/CEC.2008.4630987
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Breast cancer Feature extraction Optimization Classification algorithms Kernel Input variables
Content Type	Text
Resource Type	Article

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